Potential of Fluorescence Index Derived from the Slope Characteristics of Laser-Induced Chlorophyll Fluorescence Spectrum for Rice Leaf Nitrogen Concentration Estimation

[1]  Jinwei Dong,et al.  Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images , 2015, Scientific Reports.

[2]  C. Buschmann Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves , 2007, Photosynthesis Research.

[3]  Simone Orlandini,et al.  A simplified index for an early estimation of durum wheat yield in Tuscany (Central Italy) , 2015 .

[4]  W. Gong,et al.  Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice. , 2017, Optics express.

[5]  Mohammadmehdi Saberioon,et al.  Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. , 2016 .

[6]  Marek Zivcak,et al.  Photosynthetic responses of sun- and shade-grown barley leaves to high light: is the lower PSII connectivity in shade leaves associated with protection against excess of light? , 2014, Photosynthesis Research.

[7]  X. Yao,et al.  Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance , 2011 .

[8]  W. Gong,et al.  Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration. , 2019, Optics express.

[9]  W. Gong,et al.  Potential of vegetation indices combined with laser-induced fluorescence parameters for monitoring leaf nitrogen content in paddy rice , 2018, PloS one.

[10]  Lin Du,et al.  Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance , 2017, Remote. Sens..

[11]  Narayanan Subhash,et al.  Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in Rice Plants , 1994 .

[12]  Lilian Amorim,et al.  Gas Exchange and Emission of Chlorophyll Fluorescence during the Monocycle of Rust, Angular Leaf Spot and Anthracnose on Bean Leaves as a Function of their Trophic Characteristics , 2002 .

[13]  Anatoly A. Gitelson,et al.  Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[14]  M. Lagorio,et al.  True fluorescence spectra of leaves , 2004, Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology.

[15]  Xin Huang,et al.  Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance , 2011 .

[16]  W. Lüdeker,et al.  Remote sensing vegetation status by laser-induced fluorescence , 1994 .

[17]  Weixing Cao,et al.  Monitoring leaf nitrogen in wheat using canopy reflectance spectra , 2006 .

[18]  Z. Malenovský,et al.  Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. , 2009, Journal of experimental botany.

[19]  M Buscema,et al.  Back propagation neural networks. , 1998, Substance use & misuse.

[20]  Francesco Montemurro,et al.  Precision nitrogen management of wheat. A review , 2012, Agronomy for Sustainable Development.

[21]  K. Ali,et al.  Multivariate approach to estimate colour producing agents in Case 2 waters using first-derivative spectrophotometer data , 2014 .

[22]  Nicolas Tremblay,et al.  Sensing crop nitrogen status with fluorescence indicators. A review , 2011, Agronomy for Sustainable Development.

[23]  Fei Li,et al.  Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression , 2014 .

[24]  M. Brestič,et al.  Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency. , 2018 .

[25]  Rachel Gaulton,et al.  The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .

[26]  O. Sytar,et al.  Repetitive light pulse-induced photoinhibition of photosystem I severely affects CO2 assimilation and photoprotection in wheat leaves , 2015, Photosynthesis Research.

[27]  Marek Zivcak,et al.  Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions , 2016, Acta Physiologiae Plantarum.

[28]  Weixing Cao,et al.  Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Joseph D. Ortiz,et al.  Application of Visible/near Infrared derivative spectroscopy to Arctic paleoceanography , 2011 .

[30]  Gong Wei,et al.  Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance , 2012 .

[31]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[32]  J. Serôdio,et al.  Frequently asked questions about in vivo chlorophyll fluorescence: practical issues , 2014, Photosynthesis Research.

[33]  Lin Du,et al.  Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation , 2018, Remote. Sens..

[34]  U. Rascher,et al.  Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments , 2015, Journal of experimental botany.

[35]  Xu Chu,et al.  Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice , 2014, Plant and Soil.

[36]  R. Strasser,et al.  Drought-induced modifications of photosynthetic electron transport in intact leaves: analysis and use of neural networks as a tool for a rapid non-invasive estimation. , 2012, Biochimica et biophysica acta.